Optimal experimental design and artificial neural networks applied to the photochemically enhanced Fenton reaction

2001 ◽  
Vol 44 (5) ◽  
pp. 339-345 ◽  
Author(s):  
S. Göb ◽  
E. Oliveros ◽  
S.H. Bossmann ◽  
A.M. Braun ◽  
C.A.O. Nascimento ◽  
...  
Keyword(s):  
Neural Networks ◽  
Hydrogen Peroxide ◽  
Artificial Neural Networks ◽  
Fenton Reaction ◽  
Irradiation Time ◽  
Batch Reactor ◽  
Reaction System ◽  
Process Conditions ◽  
Minor Effect ◽  
Artificial Neural

Among advanced oxidation processes (AOPs), the photochemically enhanced Fenton reaction may be considered as one of the most efficient for the degradation of contaminants in industrial wastewater. This process involves a series of complex reactions. Therefore, an empirical model based on artificial neural networks has been developed for fitting the experimental data obtained in a laboratory batch reactor for the degradation of 2,4-dimethyl aniline (2,4-xylidine), chosen as a model pollutant. The model describes the evolution of the pollutant concentration during irradiation time as a function of the process conditions. It has been used for simulating the behavior of the reaction system in sensitivity studies aimed at optimizing the amounts of reactants employed in the process, an iron(III) salt and hydrogen peroxide, as well as the temperature. The results show that the process is most sensitive to the concentration of iron(III) salt and temperature, whereas the concentration of hydrogen peroxide has a minor effect.

Artificial neural networks modeling of non-fat yogurt texture properties: effect of process conditions and food composition

2021 ◽  
Vol 126 ◽  
pp. 164-174
Author(s):  
Laís Fernanda Batista ◽  
Clara Suprani Marques ◽  
Ana Clarissa dos Santos Pires ◽  
Luis Antônio Minim ◽  
Nilda de Fátima Ferreira Soares ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Food Composition ◽  
Process Conditions ◽  
Texture Properties ◽  
Artificial Neural

scholarly journals Prediction of Continuous Cooling Diagrams for the Precision Forged Tempering Steel 50CrMo4 by Means of Artificial Neural Networks

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Florian Nürnberger ◽  
Mirko Schaper ◽  
Friedrich-Wilhelm Bach ◽  
Iryna Mozgova ◽  
Kostjantyn Kuznetsov ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Isothermal Transformation ◽  
Process Conditions ◽  
Continuous Cooling ◽  
Artificial Neural ◽  
Transformation Diagrams ◽  
Process Energy ◽  
Usual Process ◽  
Cct Diagrams

Quenching and tempering of precision forged components using their forging heat leads to reduced process energy and shortens the usual process chains. To design such a process, neither the isothermal transformation diagrams (TTT) nor the continuous cooling transformation (CCT) diagrams from literature can be used to predict microstructural transformations during quenching since the latter diagrams are significantly influenced by previous deformations and process-related high austenitising temperatures. For this reason, deformation CCT diagrams for several tempering steels from previous works have been investigated taking into consideration the process conditions of precision forging. Within the scope of the present work, these diagrams are used as input data for predicting microstructural transformations by means of artificial neural networks. Several artificial neural network structures have been examined using the commercial software MATLAB. Predictors have been established with satisfactory capabilities for predicting CCT diagrams for different degrees of deformation within the analyzed range of data.

scholarly journals Prediction of laser drilled hole geometries from linear cutting operation by way of artificial neural networks

2021 ◽  
Author(s):  
Gabriele Baiocco ◽  
Silvio Genna ◽  
Claudio Leone ◽  
Nadia Ucciardello
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Average Power ◽  
Cutting Process ◽  
Fibre Laser ◽  
Process Conditions ◽  
Micro Drilling ◽  
Drilling Operations ◽  
Hole Geometry ◽  
Artificial Neural

AbstractThis paper deals on artificial intelligence (AI) application for the estimation of kerf geometry and hole diameters for laser micro-cutting and laser micro-drilling operations. To this aim laser cutting and laser drilling operation were performed on NIMONIC 263 superalloy sheet, 0.38 mm in nominal thickness, by way of a 100 W fibre laser in modulated wave regime. Linear cuts and holes (by trepanning) were performed fixing the average power at 80 W and changing the pulse duration, the cutting speed, the focus depth and the laser path (the latter only for the drilling operations). Kerf width and the holed diameter, at the upper and downsides, were measured by digital microscopy. Different artificial neural networks (ANNs) were developed and tested to predict the kerf widths and the diameters (at the upper and downside). Two ANNs were addressed to the linear cutting process modelling; also, two further ANNs were developed for micro-drilling on the base of the linear cutting process features. The networks were trained with a subset of data containing the process conditions and the kerf/hole geometry. The ANN test was performed with the remaining data. The results show that ANNs can model the cut and hole geometry as a function of the process parameters. Moreover, the ANN trained with kerf geometry is more efficient. Therefore, a functional correlation between the kerf geometries achievable in the linear cutting process and micro-drilling was assessed.

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